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HOLD: This will be useful when we'll want to change the method for the fingerprint computation method, but for now, it is more efficient to extract the computed fingerprint from The idea is that this is sometimes very tedious to compute efficiently and swapping methods is not straightforward because it requires modifying the metrics themselves etc. By using a fingerprint store and a fingerprint extraction abstraction, we avoid going through that route. |
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This adds an example for how to create a dataset store using a reference dataset (here we use LeMat-Bulk). A novelty metric is then implemented based on that.
The goal is to expand on this novelty metric to have a better chosen store and equivalence checker and then refine the metric.